In this third of a series of four webcasts, Professor Cathryn Lewis, from King’s College London, outlines details of a research collaboration with RGA investigating the importance of genetic information to predict morbidity and mortality outcomes. Professor Lewis presents some results of the study, using the incredible UK Biobank, predicting the onset of breast cancer and coronary artery disease.
Being able to accurately predict mortality and morbidity events is fundamental to improving the prevention, diagnosis, and treatment of a wide range of serious and life-threatening illnesses. RGA’s research collaboration with King’s College London (KCL) involves building and evaluating risk prediction models for common, complex disorders such as coronary artery disease and cancer, using data from the UK Biobank.
The UK Biobank is a population-based cohort study of 500,000 participants, which was established to identify the determinants of common life-threatening and disabling conditions. It has extensive clinical, genetic, environmental, sociodemographic, and biomarker data, with thousands of collected variables, and over 20 million genetic variants tested. The UK Biobank study offers an incredible opportunity to research mortality and major morbidity outcomes using genetic and environmental risk factors.
A particular focus of the RGA-KCL research study, presented here, is on assessing the utility of genetic information to predict disease and death. The study investigates if genetic tests provide additional risk information not captured in routinely collected clinical and biomarker data. Polygenic risk scores (PRS) are utilized to combine information across many genetic variants to give a single measure of genetic liability to disease for an individual. Professor Lewis demonstrates that PRSs have a significant contribution to risk prediction for incidence and death from breast cancer and coronary artery disease, above and beyond typical underwriting risk factors.
View additional webcasts in this series: